Computer Science Fundamentals

computer science

A broad field with several subfields is computer science. To accomplish your objectives, you need a solid foundation in computer science basics, regardless of whether you work in data science, computer networks, cryptography, web development, or another field. Regardless of where you are in your learning process, mastering the fundamentals will make you a more knowledgeable and successful developer. For instance, using knowledge in your field of interest, you might learn how to create an algorithm. However, in order to: You’ll still need to be at ease with computational thinking and other fundamental concepts.


  1. Calculate the time and space complexity of the algorithm.
  2. Utilize the existing data structures as effectively as possible.
  3. Show that the algorithm is accurate.


Choosing the right programming language for your tasks may also be necessary. To do this, you must comprehend:

  1. When to use a low-level or high-level programming language
  2. When to use an interpreter instead of a compiler in a programming language


You simply cannot address such problems without understanding the foundations of computer science, but we’ll go into more detail about these concepts later on in this essay. We’ll give you a high-level overview of three CS principles today and offer reading recommendations for each topic. Whether or not you have a degree in computer science, understanding these foundations will be helpful for theoretical computer science work, software engineering, or software development. Let’s get going!


We’ll discuss:

1. Basics of hardware and software

2. Properties of data structures

3. Algorithms: Design and Complexity

4. Advance your learning process


Basics of hardware and software


Let’s start at the very beginning: the hardware you program on and the software they execute. Computer architecture is a discipline or set of guidelines that describe how hardware and software are combined and work together to operate a computer. Two fundamental concepts—hardware and software—are introduced in that definition. Anything physically affixed to a computer is referred to as hardware. Hardware components include things like your display monitor, printer, mouse, and hard drive. This can be compared to software, which is a collection of programs and methods used to carry out activities on computers. Software is an organized set of instructions that modifies the hardware state of a machine.


Auxiliary equipment

Information is processed by a computer’s central processing unit (CPU). It is a real thing that physically pulls information out of the main memory, processes it, and then puts the updated information back in the main memory. Data flow into and out of the main memory is controlled by the control unit (CU), a component of the CPU. Another component of the CPU that processes arithmetic and logic operations is the arithmetic and logic unit (ALU). Data from the outside world or an input device is converted into streams of bytes using input units. Keyboard, mouse, microphone, camera, and USB are some examples.

  1. Output units: Render the CPU-processed data in a form that is intelligible to humans. Examples include printers, headphones, and computer monitors. After being collected and processed, data is then stored in storage units. The physical memory space is the storage device, often known as memory.
  2. Memory: Comprises secondary storage, such as hard drives, CDs, USB flash drives, etc., as well as main memory, also known as random access memory (RAM), which are actual physical memory spaces in the computer.


Computer architectures

The majority of modern computers still employ the von Neumann architecture, a 1945 invention by John von Neumann in which data and program instructions share the same memory and communication channels. In contrast to the von Neumann design, the Harvard architecture separates the signal and storage routes for data and instructions. An abstract representation of a computer is the instruction set architecture (ISA). A device that carries out the instructions provided by an ISA is an implementation. The following is typically what an ISA provides for a family of implementations:


data formats


Support for main memory management hardware

Basic characteristics

Model of input/output



What you should know about software initially

Knowledgeable areas in software include:

1. Programming language categories

Machine language: A binary stream of ones and zeros is the only language a computer can understand. A low-level programming language is machine language. Humans can understand assembly language, a low-level programming language that converts binary instructions into instructions that the computer must interpret into machine language. A link between machine language and high-level programming languages is provided by assembly languages. Programming languages also referred to as high-level languages, include Python, C++, and Java. These languages enable the development of strong, intricate, and understandable programs with a minimal number of low-level instructions (i.e. assembly language instructions).


2. Important software types

A tool that converts an assembly language program into machine language is called an assembler. Compiler: A software that primarily converts source code written in a high-level programming language into destination code that is machine-readable in a lower-level language, like machine language or assembly language. The target code is given to the target computer for execution after translation is finished. An interpreter is a program that, while the source code is being executed, incrementally converts source code written in a high-level programming language into machine-readable target code in a lower-level language.

An operating system is a piece of software that supports a computer’s fundamental operations, controls hardware and software resources, and offers shared services for software applications. User applications are pieces of software that are typically created for the end-user and are intended to do specific tasks unrelated to how a computer system works. These days, these applications can be found on the web, on mobile devices, or as standalone programs. Increase your knowledge of hardware and software ideas.


Properties of data structures


Data structures are where we’ll turn next. Data structures are formats for managing, storing, and organizing data that make it easy to access and modify that data. You use algorithms to solve problems by applying them to data structures, as we’ll cover in our third section.

An array is a group of identically typed objects that are kept in memory in order. An element is anything that is a part of an array and has an index starting with 0. Although they do not offer quick data entry or deletion, arrays are best used to retrieve data in a fixed amount of time (using an index). Study up on arrays.

Linked list: A list of connected nodes that is linear in nature. Each node in a singly linked list has a value and a pointer to the node after it in the list. Singly-linked lists lack indexes, in a contrast to arrays, therefore you must start at the root node and work your way through each node until you reach the nth node. When compared to arrays, link lists offer faster deletion and insertion but slower data retrieval. Study up on linked lists.

A non-linear data structure called a tree is frequently used to represent hierarchical data. For instance, a tree is used to arrange a hierarchical company organization. Study up on trees.

Stack: A last-in, first-out (LIFO) linear structure. Think of a stack of plates if it helps. The first plate you remove from the stack is the last one you stacked on top of it. That’s how stacks operate. Find out more about stacking. A queue and a stack are both linear data structures with variable sizes. On the other hand, queues are FIFO (first-in, first-out) data structures. Picture yourself waiting in line to ride a roller coaster. The first persons in line can head straight for the ride. Find out more about lines.

Graph: An abstract representation of the relationships between all object pairings. Explore graphs in greater detail.

Hash table: Relies on the operation of hashing, or placing an item into a certain index, also referred to as a key. The group of objects is referred to as a dictionary and each object is identifiable by a key-value pair. By putting elements in an array and using a key to identify them, a hash table is created. Using a key as input, a hash function outputs an index for which the value should be stored. Learn more hash table information.

An advanced tree-based data structure called a “heap” that is mostly used for sorting and implementing priority queues. Less is known about piles.



Algorithms: Design and Complexity

An algorithm is a set of clear instructions that inform a computer what to do in order to solve a problem, according to a computer scientist. Algorithms are used with many types of data structures, as was already noted, and they are a popular subject for coding interviews.

A study that determines an algorithm’s precise running time and is independent of the platform and input is known as asymptotic time complexity. Regardless of the underlying machine, temporal complexity analysis of this kind reveals how a program performs as the size of the input increases. We indicate the upper bound using Big O and Big Omega (Omega). Ω Big Theta (Theta) to indicate the upper bound, and Θ to signify the precise running duration limit. A general preference over an analysis based on a particular input and platform is for asymptotic time complexity.

The time complexity of recursive algorithms: Iterative algorithms’ asymptotic time complexity can be easily calculated. Recursive algorithms’ time complexity can be calculated using the substitution method, Master’s theorem, or recursion tree. The substitution approach, which is based on mathematical induction, is thought to be the most exacting of them all.

Analyzing an algorithm’s asymptotic space complexity allows for a memory usage study. The space complexity of an algorithm is also denoted by the same asymptotic notations (Big O, Big Omega, and Big Theta). Techniques for proving an algorithm’s correctness, or its ability to consistently deliver the desired results. One illustration would be demonstrating that a list will always be sorted by a sorting algorithm, regardless of the contents in the list. The term “loop invariant,” which is based on mathematical induction, refers to the most popular and commonly applied correctness technique.


Methods for designing algorithms

A technique is known as “brute force” is exploring every possibility in an effort to solve a problem. This algorithm frequently springs to mind first. It is also the least effective, therefore it frequently fails to provide the required outcome in a workable amount of time. For instance, it might take several hundred years to use brute force to break an acceptable password.

Divide and conquer is a pattern that divides a problem into smaller subtasks, solves them using recursion, and then puts everything back together. Recursion is the act of a function directly or indirectly calling itself. The merge sort and quicksort algorithms are two instances of divide and conquer strategies.

Dynamic programming: A division-and-conquer strategy. We break down complex issues into manageable pieces and then integrate their solutions. A significant distinction is that a subtask may intersect with other subtasks. As a result, we memorize the outcomes of each subtask in order to decrease running time. By memorizing, you can guarantee that each subtask is completed just once. The outcome of a subtask is promptly retrieved from memory whenever it is required again.

Greedy: A method in which we attempt to complete each subtask using the best local optimization that is currently attainable. Only when local optima lead us to the global optima—the best feasible global solution—does a greedy algorithm produce optimal outcomes. Prim’s algorithm, which determines a minimum spanning tree, and the Dijkstra algorithm, which determines the shortest path in a graph, are two examples of greedy algorithms.

Approximation algorithms provide a close to the optimal solution when it is either impractical or time-consuming to find an optimal one. Other frequently used methods for designing algorithms include linear programming and randomized algorithms.


Important algorithmic categories

Sorting and searching algorithms are used to arrange the items in a list, search for, or retrieve an item from any data structure that contains it. Mergesort, quicksort, bubble sort, selection sort, and insertion sort are a few instances of sorting. Binary search and linear search are two search examples.

Algorithms for resolving issues with networks are represented as graphs (e.g. airline flights, how the Internet is connected, social network connectivity). A graph, as previously stated, is an abstract notation that shows the relationship between all item pairings.

Finding the shortest path through a graph is a fundamental problem that is addressed by various algorithms in computer science. There are numerous sorting algorithms, each with unique benefits and drawbacks. Based on the type of data, its volume, and the user application, an algorithm is chosen.